The localization and categorization of objects in a 3D scene is a crucial component of perception systems in fields like robotics and autonomous driving. In recent years, data-driven approaches using deep neural networks have achieved superior performance in various versions of this task [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, facilitated in part by the release of numerous datasets and benchmarks [8]}, [9]}, [10]}, [11]}, [12]}, [13]}. In practical scenarios, it is important for these object detection frameworks to perform consistently well in different domain scenarios. However, deep neural networks tend to learn not only the valuable features that aid in performing the task at hand, but also the biases present in the data it is trained on. In the case of lidar datasets, the weather conditions and the location of capture lead to biases in the dataset due to the specific dimensions of roads, vehicles, and the driving conventions of the area. Additionally, different lidar sensors possess different rates of return and produce pointclouds with varying densities, leading to another set of inherent biases. This leads to a distribution gap among various pointcloud datasets.
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